Partial Block Scheme and Adaptive Update Model for Kernelized Correlation Filters-Based Object Tracking
AbstractIn visual object tracking, the dynamic environment is a challenging issue. Partial occlusion and scale variation are typical challenging problems. We present a correlation-based object tracking based on the discriminative model. To attenuate the influence by partial occlusion, partial sub-blocks are constructed from the original block, and each of them operates independently. The scale space is employed to deal with scale variation using a feature pyramid. We also present an adaptive update model with a weighting function to calculate the frame-adaptive learning rate. Theoretical analysis and experimental results demonstrate that the proposed method can robustly track drastic deformed objects. The sparse update reduces the computational cost for real-time tracking. Although the partial block scheme generation increases the computational cost, we present a novel sparse update approach to reduce the computational cost drastically for real-time tracking. The experiments were performed on a variety of sequences, and the proposed method exhibited better performance compared with the state-of-the-art trackers. View Full-Text
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Jeong, S.; Paik, J. Partial Block Scheme and Adaptive Update Model for Kernelized Correlation Filters-Based Object Tracking. Appl. Sci. 2018, 8, 1349.
Jeong S, Paik J. Partial Block Scheme and Adaptive Update Model for Kernelized Correlation Filters-Based Object Tracking. Applied Sciences. 2018; 8(8):1349.Chicago/Turabian Style
Jeong, Soowoong; Paik, Joonki. 2018. "Partial Block Scheme and Adaptive Update Model for Kernelized Correlation Filters-Based Object Tracking." Appl. Sci. 8, no. 8: 1349.
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